[1]} propose CLUE, a method for creating counterfactual explanations of uncertainty estimates from Bayesian Neural Networks.
The counterfactuals provided indicate how an input would have to be changed such that the model becomes more certain about its prediction.
While similar to what we propose, this method differs from ours in several ways.
Firstly, RETRO-VIZ does not rely on existing uncertainty estimates, but creates its own.
Therefore, RETRO-VIZ can provide explanations for any regressor, unlike CLUE which is only applicable to Bayesian models.
Secondly, the explanations we provide are more similar to feature-based explanations such as LIME [2]} or SHAP [3]} than counterfactual examples, since VIZ-explanations are framed around showing deviations in feature values as opposed to suggesting changes to feature values that result in alternative predictions.
